from bluepyopt.ephys.responses import TimeVoltageResponse
from bluepyparallel import init_parallel_factory
import yaml
from functools import partial

from pathlib import Path
import matplotlib.pyplot as plt
import pickle

# from emodel_generalisation.utils import plot_traces
from emodel_generalisation.model.modifiers import synth_soma, synth_axon
from emodel_generalisation.mcmc import load_chains
from emodel_generalisation.mcmc import save_selected_emodels
import logging
from itertools import cycle
import json
from matplotlib.backends.backend_pdf import PdfPages
import pandas as pd
from emodel_generalisation.model.access_point import AccessPoint
from emodel_generalisation.model.evaluation import feature_evaluation
from emodel_generalisation.utils import get_combo_hash
from datareuse import Reuse
import extra_features


def plot_traces(trace_df, trace_path="traces", pdf_filename="traces.pdf"):
    """Plot traces from df, with highlighs on rows with trace_highlight = True.

    Args:
        trace_df (DataFrame): contains list of combos with traces to plot
        trace_path (str): path to folder with traces in .pkl
        pdf_filename (str): name of pdf to save
    """
    COLORS = cycle(["r"] + [f"C{i}" for i in range(10)])
    trace_df = trace_df.copy()  # prevents annyoing panda warnings
    if "trace_highlight" not in trace_df.columns:
        trace_df["trace_highlight"] = True
    for index in trace_df.index:
        if trace_df.loc[index, "trace_highlight"]:
            c = next(COLORS)

        if "trace_data" in trace_df.columns:
            trace_path = trace_df.loc[index, "trace_data"]
        else:
            combo_hash = get_combo_hash(trace_df.loc[index])
            trace_path = Path(trace_path) / ("trace_id_" + str(combo_hash) + ".pkl")

        with open(trace_path, "rb") as f:
            trace = pickle.load(f)
            if isinstance(trace, list):
                trace = trace[1]  # newer version the response are here
            for protocol, response in trace.items():
                lw = 0.8

                plt.figure(protocol, figsize=(10, 6))
                # plt.gca().set_xlim(5000, 6500)
                # if protocol == 'Step_ReboundBurst_burst.soma.ina':
                #    plt.gca().set_ylim(-60000, 0)
                plt.plot(
                    response["time"],
                    response["voltage"],
                    # label=label,
                    c="0.5",
                    lw=lw,
                )

                plt.xlabel("Time (ms)")
                plt.ylabel("Voltage (mV)")
    with PdfPages(pdf_filename) as pdf:
        for fig_id in plt.get_fignums():
            fig = plt.figure(fig_id)
            plt.legend(loc="best")
            plt.suptitle(fig.get_label())
            pdf.savefig()
            plt.close()


if __name__ == "__main__":
    parallel_factory = init_parallel_factory("multiprocessing")
    # parallel_factory = init_parallel_factory("dask_dataframe")
    emodel = "simplest"
    logger = logging.getLogger()
    v = 1
    logging.basicConfig(
        level=(logging.WARNING, logging.INFO, logging.DEBUG)[v],
        handlers=[logging.StreamHandler()],
    )
    logger.setLevel((logging.WARNING, logging.INFO, logging.DEBUG)[v])

    access_point = AccessPoint(
        emodel_dir=".",
        recipes_path="config/recipes.json",
        final_path="final.json",
        with_seeds=True,
    )
    exemplar_data = yaml.safe_load(open("../../../mcmc_run/exemplar_data.yaml"))
    access_point.morph_path = exemplar_data["paths"]["all"]
    access_point.settings["morph_modifiers"] = [
        partial(synth_soma, params=exemplar_data["soma"], scale=1.0),
        partial(synth_axon, params=exemplar_data["ais"]["popt"], scale=1.0),
    ]

    from emodel_generalisation.utils import get_feature_df

    emodels = ["ecel", "spp", "runaway"]
    bns = []
    for emodel in emodels:
        df = pd.read_csv("morphologies.csv")
        df["emodel"] = f"simplest_{emodel}"
        df["name"] = "exemplar"
        print(df)
        Path("traces").mkdir(exist_ok=True)
        with Reuse(f"eval_traces_{emodel}.csv") as reuse:
            df = reuse(
                feature_evaluation,
                df,
                access_point,
                parallel_factory=parallel_factory,
                trace_data_path="traces",
                # record_ions_and_currents=True,
            )
        plot_traces(df, pdf_filename=f"traces_{emodel}.pdf")
        plt.close("all")
        d = get_feature_df(df)
        _d = d[["Step_ReboundBurst_burst.soma.v.all_burst_number"]]
        _d["emodel"] = emodel
        bns.append(_d)

    bns = pd.concat(bns)
    import seaborn as sns

    plt.figure(figsize=(5, 3))
    sns.boxplot(
        data=bns,
        y="emodel",
        x="Step_ReboundBurst_burst.soma.v.all_burst_number",
        ax=plt.gca(),
        orient="h",
        showfliers=False,
        #color="k",
    )
    sns.stripplot(
        data=bns,
        y="emodel",
        x="Step_ReboundBurst_burst.soma.v.all_burst_number",
        ax=plt.gca(),
        orient="h",
        size=4,
        color="k",
    )
    plt.xlabel('burst number')
    plt.tight_layout()

    plt.savefig("population_burst_numbers.pdf")
